U.S. patent number 10,831,557 [Application Number 16/289,462] was granted by the patent office on 2020-11-10 for task management using a virtual node.
This patent grant is currently assigned to International Business Machines Corporation. The grantee listed for this patent is International Business Machines Corporation. Invention is credited to Lin Cai, Chenghui Chen, Di Ling Chen, Ting SH Li, Dong Wen, Ming Yang, Yiming Yin.
United States Patent |
10,831,557 |
Cai , et al. |
November 10, 2020 |
Task management using a virtual node
Abstract
Provided is a method, system, and computer program product for
managing tasks in a computing system using a virtual node. A
processor may register a virtual node for handling tasks allocated
by a scheduling node in a computing system, the computing system
comprising the scheduling node and a group of actual computing
nodes processing tasks allocated by the scheduling node, and the
scheduling node takes the virtual node as an actual computing node.
A performance level of the computing system is obtained. Capacity
of the virtual node is set based on the obtained performance level,
such that the scheduling node allocates tasks to the virtual node
based on the capacity of the virtual node. In response to at least
one task being allocated by the scheduling node to the virtual
node, the at least one task is received by the virtual node.
Inventors: |
Cai; Lin (Shanghai,
CN), Chen; Di Ling (Beijing, CN), Li; Ting
SH (Shanghai, CN), Yin; Yiming (Shanghai,
CN), Chen; Chenghui (Beijing, CN), Yang;
Ming (Shanghai, CN), Wen; Dong (Beijing,
CN) |
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Assignee: |
International Business Machines
Corporation (Armonk, NY)
|
Family
ID: |
1000005173952 |
Appl.
No.: |
16/289,462 |
Filed: |
February 28, 2019 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
|
US 20200278889 A1 |
Sep 3, 2020 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F
9/45558 (20130101); G06F 9/5077 (20130101); G06F
9/4881 (20130101); G06F 2009/4557 (20130101); G06F
2009/45595 (20130101) |
Current International
Class: |
G06F
9/455 (20180101); G06F 9/50 (20060101); G06F
9/48 (20060101) |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Patil, Shital et al. "Improving Performance Guarantees in Cloud
Computing through Efficient and Dynamic Task Scheduling".
International Journal of Advanced Computer Research. vol. 2 No. 4
Issue 6. (Year: 2012). cited by examiner .
Cai et al., "Task Management Using a Virtual Node," U.S. Appl. No.
16/512,479, filed Jul. 16, 2019. cited by applicant .
List of IBM Patents or Patent Applications Treated as Related, Jul.
15, 2019, 2 pgs. cited by applicant .
Github, "Kubemark User Guide,"
https://github.com/eBay/Kubernetes/blob/master/docs/devel/kubemark-guide.-
md, Nov. 25, 2015, printed Jan. 3, 2019, 4 pgs. cited by applicant
.
Kubenetes, "Advanced Scheduling in Kubernetes,"
https://kubernetes.io/blog/2017/03/advanced-scheduling-in-kubernetes/,
Mar. 31, 2017, printed Jan. 3, 2019, 7 pgs. cited by applicant
.
Mell et al., "The NIST Definition of Cloud Computing,"
Recommendations of the National Institute of Standards and
Technology, U.S. Department of Commerce, Special Publication
800-145, Sep. 2011, 7 pgs. cited by applicant .
Unknown, "Auto-repairing nodes,"
https://cloud.google.com/kubernetes-engine/docs/how-to/node-auto-repair,
Google Cloud, Kubernetes Engine, last updated Sep. 25, 2018, 3 pgs.
cited by applicant .
Wang et al., "Approaches for Resilience Against Cascading Failures
in Cloud Datacenters,"
https://ieeexplore.ieee.org/abstract/document/8416337/similar#similar,
2018 IEEE 38th International Conference on Distributed Computing
Systems, Jul. 2-6, 2018, pp. 706-717. cited by applicant.
|
Primary Examiner: Lee; Adam
Attorney, Agent or Firm: Suchecki; Peter K.
Claims
What is claimed is:
1. A computer-implemented system, comprising a computer processor
coupled to a computer-readable memory unit, the computer-readable
memory unit comprising instructions that when executed by the
computer processor implements a method comprising: registering a
virtual node with a scheduling node, wherein the virtual node is
configured to receive tasks allocated by the scheduling node in the
computer-implemented system, wherein the the computer-implemented
system further comprises the virtual node, the scheduling node, and
a group of actual computing nodes processing tasks allocated by the
scheduling node, and wherein the scheduling node treats the virtual
node as an actual computing node; obtaining a performance level of
the computer-implemented system based in part on an average
response time of the group of actual computing nodes; setting a
capacity of the virtual node to receive tasks from the scheduling
node based on the obtained performance level of the
computer-implemented system meeting a criterion, wherein the
scheduling node allocates tasks to the virtual node and/or the
group of actual computing nodes based on the capacity of the
virtual node; storing, in response to at least one task being
allocated by the scheduling node to the virtual node, the at least
one task in the virtual node, wherein the virtual node does not
process the at least one task; and processing, in response to at
least one task being allocated by the scheduling node to the group
of actual computing nodes, the at least one task by the group of
actual computing nodes.
2. The computer-implemented system of claim 1, wherein when the at
least one task is stored by the virtual node, re-allocating the at
least on task to the group of actual computing nodes by the
scheduling node.
3. The computer-implemented system of claim 1, wherein setting the
capacity of the virtual node based on the obtained performance
level of the computer-implemented system meeting the criterion
comprises: setting, in response to the obtained performance level
of the computer-implemented system meeting a first criterion
indicating a low performance level of the group of actual computing
nodes, a strong capacity to the virtual node, wherein in response
to setting the strong capacity the scheduling node allocates tasks
to the virtual node.
4. The computer-implemented system of claim 1, wherein setting the
capacity of the virtual node based on the obtained performance
level of the computer-implemented system meeting the criterion
comprises: requesting, in response to the obtained performance
level of the computer-implemented system meeting a second criterion
indicating a normal performance level of the group of actual
computing nodes and at least one task being held in the virtual
node, the scheduling node to re-allocate the at least one task from
the virtual node to the group of actual computing nodes, wherein
responsive to the requesting, the scheduling node reallocates the
at least one task from the virtual node to the group of actual
computing nodes.
5. The computer-implemented system of claim 1, wherein setting the
capacity of the virtual node based on the obtained performance
level of the computer-implemented system meeting the criterion
comprises: setting, in response to the obtained performance level
of the computer-implemented system meeting a third criterion
indicating a high performance level of the group of actual
computing nodes and no task being held in the virtual node, a weak
capacity to the virtual node, wherein in response to setting the
weak capacity, preventing tasks from being allocated to the virtual
node by the scheduling node.
6. The computer-implemented system of claim 1, wherein the average
response time of the group of actual computing nodes is a time
duration between a starting time when the computer-implemented
system receives a task and an ending time when the scheduling node
allocates the received task to at least one actual computing node
and the at least one actual computing node finishes processing the
received task.
7. A computer program product comprising a computer readable
storage medium having program instructions embodied therewith, the
program instructions executable by a processor to cause the
processor to perform a method comprising: registering a virtual
node with a scheduling node, wherein the virtual node is configured
to receive tasks allocated by the scheduling node in a computing
system, wherein the computing system comprises the virtual node,
the scheduling node, and a group of actual computing nodes
processing tasks allocated by the scheduling node, and wherein the
scheduling node treats the virtual node as an actual computing
node; obtaining a performance level of the computing system based
in part on an average response time of the group of actual
computing nodes; setting a capacity of the virtual node to receive
tasks from the scheduling node based on the obtained performance
level of the computing system meeting a criterion, wherein the
scheduling node allocates tasks to the virtual node and/or the
group of actual computing nodes based on the capacity of the
virtual node; storing, in response to at least one task being
allocated by the scheduling node to the virtual node, the at least
one task in the virtual node, wherein the virtual node does not
process the at least one task; and processing, in response to at
least one task being allocated by the scheduling node to the group
of actual computing nodes, the at least one task by the group of
actual computing nodes.
8. The computer program product of claim 7, wherein when the at
least one task is stored by the virtual node, re-allocating the at
least on task to the group of actual computing nodes by the
scheduling node.
9. The computer program product of claim 7, wherein setting the
capacity of the virtual node based on the obtained performance
level of the computing system meeting the criterion comprises:
setting, in response to the obtained performance level of the
computing system meeting a first criterion indicating a low
performance level of the group of actual computing nodes, a strong
capacity to the virtual node, wherein in response to setting the
strong capacity the scheduling node allocates tasks to the virtual
node.
10. The computer program product of claim 7, wherein setting the
capacity of the virtual node based on the obtained performance
level of the computing system meeting the criterion comprises:
requesting, in response to the obtained performance level of the
computing system meeting a second criterion indicating a normal
performance level of the group of actual computing nodes and at
least one task being held in the virtual node, the scheduling node
to re-allocate the at least one task from the virtual node to the
group of actual computing nodes, wherein responsive to the
requesting, the scheduling node reallocates the at least one task
from the virtual node to the group of actual computing nodes.
11. The computer program product of claim 7, wherein setting the
capacity of the virtual node based on the obtained performance
level of the computing system meeting the criterion comprises:
setting, in response to the obtained performance level of the
computing system meeting a third criterion indicating a high
performance level of the group of actual computing nodes and no
task being held in the virtual node, a weak capacity to the virtual
node, wherein in response to setting the weak capacity, preventing
tasks from being allocated to the virtual node by the scheduling
node.
12. The computer program product of claim 7, wherein the average
response time of the group of actual computing nodes is a time
duration between a starting time when the computing system receives
a task and an ending time when the scheduling node allocates the
received task to at least one actual computing node and the at
least one actual computing node finishes processing the received
task.
Description
BACKGROUND
The present disclosure relates generally to managing tasks within
distributed computing systems, and more specifically, to mitigating
cascade failures by managing tasks using a virtual node.
With the development of distributed computing, tasks may be
distributed by a scheduling node to multiple actual computing nodes
included in the distributed computing system. During operations of
the distributed computing system, states of the multiple actual
computing nodes may vary due to their hardware and software
configurations. An actual computing node may be a physical computer
or a virtual machine which is running on a physical computer and
sharing resources with other virtual machines. In a cloud system,
either the physical computer or the virtual machine can be a node
that handles processing tasks. A virtual node may be an application
that mimics a node. However, the virtual node may not handle
processing actual tasks.
SUMMARY
Embodiments of the present disclosure include a
computer-implemented method for mitigating cascade failures by
managing tasks using a virtual node. According to the method, a
virtual node is registered for handling tasks allocated by a
scheduling node in a computing system, the computing system
comprises the scheduling node and a group of actual computing nodes
processing tasks allocated by the scheduling node, and the
scheduling node takes the virtual node as an actual computing node.
A performance level of the computing system is obtained. Capacity
of the virtual node is set based on the obtained performance level,
such that the scheduling node allocates tasks to the virtual node
based on the capacity of the virtual node. In response to at least
one task being allocated by the scheduling node to the virtual
node, the at least one task is received.
Embodiments of the present disclosure include a
computer-implemented system for mitigating cascade failures by
managing tasks using a virtual node. The computing system comprises
a computer processor coupled to a computer-readable memory unit,
where the memory unit comprises instructions that when executed by
the computer processor implements the above method.
Embodiments of the present disclosure may be directed toward a
computer program product for mitigating cascade failures by
managing tasks using a virtual node. The computer program product
comprises a computer readable storage medium having program
instructions embodied therewith. The program instructions are
executable by a processor to cause the processor to perform actions
of the above method.
Further aspects of the present disclosure are directed toward a
system and computer program product with functionality similar to
the functionality discussed above regarding the
computer-implemented method. The present summary is not intended to
illustrate each aspect of, every implementation of, and/or every
embodiment of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
The drawings included in the present application are incorporated
into, and form part of, the specification. They illustrate
embodiments of the present disclosure and, along with the
description, serve to explain the principles of the disclosure. The
drawings are only illustrative of certain embodiments and do not
limit the disclosure.
FIG. 1 depicts a cloud computing node according to an embodiment of
the present disclosure.
FIG. 2 depicts a cloud computing environment according to an
embodiment of the present invention.
FIG. 3 depicts abstraction model layers according to an embodiment
of the present invention.
FIG. 4 depicts an example computing system comprising multiple
actual computing nodes and a scheduling node according to an
existing solution, in accordance with embodiments of the present
disclosure.
FIG. 5 depicts an example diagram for managing tasks in a computing
system comprising a virtual node, in accordance with embodiments of
the present disclosure.
FIG. 6 depicts an example flowchart of a method for managing tasks,
in accordance with embodiments of the present disclosure.
FIG. 7 depicts an example method for management of the virtual node
based on performance information of a computing system, in
accordance with embodiments of the present disclosure.
FIG. 8 depicts an example diagram for setting capacity for the
virtual node, in accordance with embodiments of the present
disclosure.
FIG. 9 depicts an example diagram for holding a task in a task
queue associated with the virtual node, in accordance with
embodiments of the present disclosure.
FIG. 10 depicts an example diagram for releasing a task in a task
queue associated with the virtual node, in accordance with
embodiments of the present disclosure.
While the present disclosure is amenable to various modifications
and alternative forms, specifics thereof have been shown by way of
example in the drawings and will be described in detail. It should
be understood, however, that the intention is not to limit the
present disclosure to the particular embodiments described. On the
contrary, the intention is to cover all modifications, equivalents,
and alternatives falling within the spirit and scope of the present
disclosure.
DETAILED DESCRIPTION
Aspects of the present disclosure relate to managing tasks within
distributed computing systems, and more specifically, to mitigating
cascade failures by managing tasks using a virtual node. While not
limited to such applications, embodiments of the present disclosure
may be better understood in light of the aforementioned
context.
Some embodiments will be described in more detail with reference to
the accompanying drawings, in which the embodiments of the present
disclosure have been illustrated. However, the present disclosure
can be implemented in various manners, and thus should not be
construed to be limited to the embodiments disclosed herein.
It is to be understood that although this disclosure includes a
detailed description on cloud computing, implementation of the
teachings recited herein are not limited to a cloud computing
environment. Rather, embodiments of the present disclosure are
capable of being implemented in conjunction with any other type of
computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling
convenient, on-demand network access to a shared pool of
configurable computing resources (e.g. networks, network bandwidth,
servers, processing, memory, storage, applications, virtual
machines, and services) that can be rapidly provisioned and
released with minimal management effort or interaction with a
provider of the service. This cloud model may include at least five
characteristics, at least three service models, and at least four
deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision
computing capabilities, such as server time and network storage, as
needed automatically without requiring human interaction with the
service's provider.
Broad network access: capabilities are available over a network and
accessed through standard mechanisms that promote use by
heterogeneous thin or thick client platforms (e.g., mobile phones,
laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to
serve multiple consumers using a multi-tenant model, with different
physical and virtual resources dynamically assigned and reassigned
according to demand. There is a sense of location independence in
that the consumer generally has no control or knowledge over the
exact location of the provided resources but may be able to specify
location at a higher level of abstraction (e.g., country, state, or
datacenter).
Rapid elasticity: capabilities can be rapidly and elastically
provisioned, in some cases automatically, to quickly scale out and
rapidly released to quickly scale in. To the consumer, the
capabilities available for provisioning often appear to be
unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize
resource use by leveraging a metering capability at some level of
abstraction appropriate to the type of service (e.g., storage,
processing, bandwidth, and active user accounts). Resource usage
can be monitored, controlled, and reported providing transparency
for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the
consumer is to use the provider's applications running on a cloud
infrastructure. The applications are accessible from various client
devices through a thin client interface such as a web browser
(e.g., web-based e-mail). The consumer does not manage or control
the underlying cloud infrastructure including network, servers,
operating systems, storage, or even individual application
capabilities, with the possible exception of limited user-specific
application configuration settings.
Platform as a Service (PaaS): the capability provided to the
consumer is to deploy onto the cloud infrastructure
consumer-created or acquired applications created using programming
languages and tools supported by the provider. The consumer does
not manage or control the underlying cloud infrastructure including
networks, servers, operating systems, or storage, but has control
over the deployed applications and possibly application hosting
environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the
consumer is to provision processing, storage, networks, and other
fundamental computing resources where the consumer is able to
deploy and run arbitrary software, which can include operating
systems and applications. The consumer does not manage or control
the underlying cloud infrastructure but has control over operating
systems, storage, deployed applications, and possibly limited
control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an
organization. It may be managed by the organization or a third
party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several
organizations and supports a specific community that has shared
concerns (e.g., mission, security requirements, policy, and
compliance considerations). It may be managed by the organizations
or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the
general public or a large industry group and is owned by an
organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or
more clouds (private, community, or public) that remain unique
entities but are bound together by standardized or proprietary
technology that enables data and application portability (e.g.,
cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on
statelessness, low coupling, modularity, and semantic
interoperability. At the heart of cloud computing is an
infrastructure that includes a network of interconnected nodes.
Referring now to FIG. 1, a schematic of an example of a cloud
computing node is shown. Cloud computing node 10 is only one
example of a suitable cloud computing node and is not intended to
suggest any limitation as to the scope of use or functionality of
embodiments of the invention described herein. Regardless, cloud
computing node 10 is capable of being implemented and/or performing
any of the functionality set forth hereinabove.
In cloud computing node 10 there is a computer system/server 12 or
a portable electronic device such as a communication device, which
is operational with numerous other general purpose or special
purpose computing system environments or configurations. Examples
of well-known computing systems, environments, and/or
configurations that may be suitable for use with computer
system/server 12 include, but are not limited to, personal computer
systems, server computer systems, thin clients, thick clients,
hand-held or laptop devices, multiprocessor systems,
microprocessor-based systems, set top boxes, programmable consumer
electronics, network PCs, minicomputer systems, mainframe computer
systems, and distributed cloud computing environments that include
any of the above systems or devices, and the like.
Computer system/server 12 may be described in the general context
of computer system-executable instructions, such as program
modules, being executed by a computer system. Generally, program
modules may include routines, programs, objects, components, logic,
data structures, and so on that perform particular tasks or
implement particular abstract data types. Computer system/server 12
may be practiced in distributed cloud computing environments where
tasks are performed by remote processing devices that are linked
through a communications network. In a distributed cloud computing
environment, program modules may be located in both local and
remote computer system storage media including memory storage
devices.
As shown in FIG. 1, computer system/server 12 in cloud computing
node 10 is shown in the form of a general-purpose computing device.
The components of computer system/server 12 may include, but are
not limited to, one or more processors or processing units 16, a
system memory 28, and a bus 18 that couples various system
components including system memory 28 to processor 16.
Bus 18 represents one or more of any of several types of bus
structures, including a memory bus or memory controller, a
peripheral bus, an accelerated graphics port, and a processor or
local bus using any of a variety of bus architectures. By way of
example, and not limitation, such architectures include Industry
Standard Architecture (ISA) bus, Micro Channel Architecture (MCA)
bus, Enhanced ISA (EISA) bus, Video Electronics Standards
Association (VESA) local bus, and Peripheral Component Interconnect
(PCI) bus.
Computer system/server 12 typically includes a variety of computer
system readable media. Such media may be any available media that
is accessible by computer system/server 12, and it includes both
volatile and non-volatile media, removable and non-removable
media.
System memory 28 can include computer system readable media in the
form of volatile memory, such as random access memory (RAM) 30
and/or cache memory 32. Computer system/server 12 may further
include other removable/non-removable, volatile/non-volatile
computer system storage media. By way of example only, storage
system 34 can be provided for reading from and writing to a
non-removable, non-volatile magnetic media (not shown and typically
called a "hard drive"). Although not shown, a magnetic disk drive
for reading from and writing to a removable, non-volatile magnetic
disk (e.g., a "floppy disk"), and an optical disk drive for reading
from or writing to a removable, non-volatile optical disk such as a
CD-ROM, DVD-ROM or other optical media can be provided. In such
instances, each can be connected to bus 18 by one or more data
media interfaces. As will be further depicted and described below,
memory 28 may include at least one program product having a set
(e.g., at least one) of program modules that are configured to
carry out the functions of embodiments of the invention.
Program/utility 40, having a set (at least one) of program modules
42, may be stored in memory 28 by way of example, and not
limitation, as well as an operating system, one or more application
programs, other program modules, and program data. Each of the
operating system, one or more application programs, other program
modules, and program data or some combination thereof, may include
an implementation of a networking environment. Program modules 42
generally carry out the functions and/or methodologies of
embodiments of the invention as described herein.
Computer system/server 12 may also communicate with one or more
external devices 14 such as a keyboard, a pointing device, a
display 24, etc.; one or more devices that enable a user to
interact with computer system/server 12; and/or any devices (e.g.,
network card, modem, etc.) that enable computer system/server 12 to
communicate with one or more other computing devices. Such
communication can occur via Input/Output (I/O) interfaces 22. Still
yet, computer system/server 12 can communicate with one or more
networks such as a local area network (LAN), a general wide area
network (WAN), and/or a public network (e.g., the Internet) via
network adapter 20. As depicted, network adapter 20 communicates
with the other components of computer system/server 12 via bus 18.
It should be understood that although not shown, other hardware
and/or software components could be used in conjunction with
computer system/server 12. Examples include, but are not limited
to: microcode, device drivers, redundant processing units, external
disk drive arrays, RAID systems, tape drives, and data archival
storage systems, etc.
Referring now to FIG. 2, illustrative cloud computing environment
50 is depicted. As shown, cloud computing environment 50 includes
one or more cloud computing nodes 10 with which local computing
devices used by cloud consumers, such as, for example, personal
digital assistant (PDA) or cellular telephone 54A, desktop computer
54B, laptop computer 54C, and/or automobile computer system 54N may
communicate. Nodes 10 may communicate with one another. They may be
grouped (not shown) physically or virtually, in one or more
networks, such as Private, Community, Public, or Hybrid clouds as
described hereinabove, or a combination thereof. This allows cloud
computing environment 50 to offer infrastructure, platforms and/or
software as services for which a cloud consumer does not need to
maintain resources on a local computing device. It is understood
that the types of computing devices 54A-N shown in FIG. 2 are
intended to be illustrative only and that computing nodes 10 and
cloud computing environment 50 can communicate with any type of
computerized device over any type of network and/or network
addressable connection (e.g., using a web browser).
Referring now to FIG. 3, a set of functional abstraction layers
provided by cloud computing environment 50 (FIG. 2) is shown. It
should be understood in advance that the components, layers, and
functions shown in FIG. 3 are intended to be illustrative only and
embodiments of the invention are not limited thereto. As depicted,
the following layers and corresponding functions are provided:
Hardware and software layer 60 includes hardware and software
components. Examples of hardware components include: mainframes 61;
RISC (Reduced Instruction Set Computer) architecture based servers
62; servers 63; blade servers 64; storage devices 65; and networks
and networking components 66. In some embodiments, software
components include network application server software 67 and
database software 68.
Virtualization layer 70 provides an abstraction layer from which
the following examples of virtual entities may be provided: virtual
servers 71; virtual storage 72; virtual networks 73, including
virtual private networks; virtual applications and operating
systems 74; and virtual clients 75.
In one example, management layer 80 may provide the functions
described below. Resource provisioning 81 provides dynamic
procurement of computing resources and other resources that are
utilized to perform tasks within the cloud computing environment.
Metering and Pricing 82 provide cost tracking as resources are
utilized within the cloud computing environment, and billing or
invoicing for consumption of these resources. In one example, these
resources may include application software licenses. Security
provides identity verification for cloud consumers and tasks, as
well as protection for data and other resources. User portal 83
provides access to the cloud computing environment for consumers
and system administrators. Service level management 84 provides
cloud computing resource allocation and management such that
required service levels are met. Service Level Agreement (SLA)
planning and fulfillment 85 provide pre-arrangement for, and
procurement of, cloud computing resources for which a future
requirement is anticipated in accordance with an SLA.
Workloads layer 90 provides examples of functionality for which the
cloud computing environment may be utilized. Examples of workloads
and functions which may be provided from this layer include:
mapping and navigation 91; software development and lifecycle
management 92; virtual classroom education delivery 93; data
analytics processing 94; transaction processing 95; and task
management 96.
It should be noted that the processing of task management according
to embodiments of this disclosure could be implemented by computer
system/server 12 of FIG. 1. Hereinafter, reference will be made to
FIG. 4 to FIG. 10 to describe details of the task management
96.
Sometimes, when performance of a computing node degrades or a
failure occurs in the computing node in a distributed computing
system, tasks that have been allocated to the computing node should
be re-distributed to other normal computing nodes. At this point,
workloads of the other normal computing nodes may increase and
performance of the computing system may be affected.
For the sake of description, embodiments of the present invention
will be described in an environment of a distributed computing
system in FIG. 4. In the context of the present disclosure, a
distributed computing system may also be referred to as a computing
system. FIG. 4 depicts an example distributed computing system 400,
and the computing system 400 may comprise a scheduling node 450 and
a group of actual computing nodes 420, 430, . . . , and 440. Here,
the scheduling node 450 and the group of actual computing nodes
420, 430, . . . , and 440 may be connected via a network 410. It is
to be understood that the scheduling node 450 and the actual
computing nodes 420, 430, . . . , and 440 may be implemented by
physical devices, logical devices or a combination thereof. The
computing system 400 may receive requests from users through a user
interface (not shown in FIG. 4) on the scheduling node 450 or
anyone from the actual computing nodes 420, 430, . . . , and 440.
Then, the scheduling node 450 may allocate tasks initiated by the
requests to one or more nodes in the group of actual computing
nodes 420, 430, . . . , and 440.
Sometimes, a failure may occur in the computing system 400. For
example, the actual computing node 430 may fail due to a peak
workload or an exception in the software or hardware configuration
in the actual computing node 430, and the like. As depicted by
arrows 470 and 472, the tasks that have been previously allocated
to the abnormal computing node 430 may be re-allocated to other
normal computing nodes 420 and 440, respectively. Therefore,
performance of the actual computing nodes 420 and 440 may be
decreased as they handle the additional tasks from failed computing
node 430. If the actual computing nodes 420 and 440 cannot process
the re-allocated tasks within a reasonable time period, they may
transfer the tasks to other actual computing nodes via the network
410 (as depicted by arrows 460 and 462), which may cause a
cascading re-allocation of the tasks among the actual computing
nodes and may result in a low performance of the computing system
400.
There are some proposed solutions for managing tasks in the
computing system 400. According to one solution, powerful hardware
may be deployed in the actual computing nodes to extend the
processing capability of the computing nodes during a peak time.
However, this solution increases the cost as the powerful
processing capability is wasted during a normal operation of the
computing system 400. According to another solution, a scheduling
mechanism for the scheduler 450 may be modified to alleviate
effects of task re-allocation. However, this solution is difficult
to be implemented because it requires modifying implementations of
the scheduling node 450 and also affects implementations of the
actual computing nodes 420, 430, . . . , and 440.
In view of the above, the present disclosure provides a solution
for managing tasks in the computing system. Hereinafter, reference
will be made to FIG. 5 for a general description of embodiments of
the present disclosure. FIG. 5 depicts an example diagram for
managing tasks in a computing system 500 comprising a virtual node
510, in accordance with embodiments of the present disclosure. As
illustrated in FIG. 5, the scheduling node 450, the actual
computing nodes 420, 430, . . . , and 440 are the same as those in
the computing system 400 in FIG. 4. Further, a virtual node 510 may
register into the computing system 500. Here, the virtual node 510
may be a dumb node implemented by an application imitating some or
all of the behaviors of an actual computing node.
For example, the virtual node 510 may exhibit its processing
resources (such as a processor) and memory resources (such as a
memory) for processing tasks to the scheduling node 450, such that
the scheduling node 450 may treat the virtual node 510 as an actual
computing node and allocate task(s) to the virtual node 510. Here,
from the perspective of the scheduling node 450, the virtual node
510 may be used to handle at least one task that is to be allocated
by the scheduling node 450 in the computing system 500. However,
the virtual node 510 only accepts task(s) allocated to itself and
keeps the allocated task(s) in a storage, but it does not really
process the allocated task(s).
In the above embodiments of the present disclosure, by accepting
the registration of the virtual node 510 for the computing system
500, implementations of the other computing nodes 420, 430, . . . ,
and 440 and the scheduling node 450 do not need to be modified.
Accordingly, this embodiment provides an easy and effective way for
managing tasks in the computing system 500. Moreover, the virtual
node 510 may absorb excessive tasks that cannot be processed by the
actual computing nodes 420, 430, . . . , and 440. Therefore, the
performance of the actual computing nodes 420, 430, . . . , and 440
may not be interfered with by the excessive tasks.
Hereinafter, reference will be made to FIG. 6 for details of the
present disclosure. FIG. 6 depicts an example flowchart of a method
600 for managing tasks, in accordance with embodiments of the
present disclosure. At block 610, the virtual node 510 may register
with the scheduling node 450 for handling at least one task that is
to be allocated by the scheduling node 450 in the computing system
500. Here, the computing system 500 may comprise the scheduling
node 450 and a group of actual computing nodes 420, 430, . . . ,
and 440 processing tasks allocated by the scheduling node 450.
In this embodiment, the virtual node 510 may be implemented by an
individual application that is installed on a computing device. For
example, the application may be installed on any of the actual
computing nodes, the scheduling node 450, or another device. In
another embodiment, the virtual node 510 may be implemented by a
procedure in an application for managing the tasks in the computing
system 500. Here, the application may be implemented on any of a
physical device or a logical device, as along as the virtual node
510 can communicate with other nodes in the computing system 500.
In some embodiments, the application may be launched in advance. In
some embodiments, the application may be launched just before the
registering step. After the registration of the virtual node, the
scheduling node 450 may be notified that the virtual node 510 has
joined the computing system 500. The scheduling node 450 does not
need to know that the virtual node 510 is different from an actual
computing node; the scheduling node 450 just allocates tasks to the
virtual node as it does other actual computing nodes.
According to embodiments of the present disclosure, the virtual
node 510 may work as an actual computing node from the perspective
of the scheduling node, although the virtual node 510 itself does
not really process any task. The virtual node 510 may exhibit
capacity to the outside and make the scheduling node 450 believe
that it is able to process task(s) allocated to it. The capacity
may comprise various aspects of the workload declared by the
virtual node 510, such as usage of processing resources and memory
resources, and the like. In some embodiments, the capacity may be
represented by a usage ratio of processing resources, memory
resources, or a combination thereof. Based on the declared
capacity, the scheduling node 450 may allocate tasks among the
group of actual computing nodes 420, 430, . . . , and 440 and the
virtual node 450.
At block 620, a performance level of the computing system 500 may
be obtained by the virtual node 510. Here, the performance level
may be obtained in various ways. In some embodiments of the present
disclosure, the performance level of the computing system 500 may
be evaluated by average response time of the computing system 500.
Specifically, the average response time may be a time duration
between a starting time when the computing system 500 receives a
task and an ending time when the scheduling node 450 allocates the
received task to at least one specific computing node and the at
least one specific computing node finishes processing the received
task.
According to embodiments of the present disclosure, the average
response time may be determined based on historical operations of
the computing system 500. In one example, if the computing system
500 allocated 100 tasks to the group of computing nodes in the
computing system 500, and the group of computing nodes finish
processing these tasks within 5 seconds, then the average response
time may be determined as 5/100=0.05 second. The lower the average
response time is, the higher the performance level of the computing
system 500 is. In another example, the performance level of the
computing system 500 may be determined according to how many tasks
may be processed within a certain time duration. Supposing the
group of computing nodes in the computing system 500 finishes
processing 100 tasks within 5 seconds, then the computing system
500 may handle 100/5=20 tasks within one second. The lower the
number of the tasks is, the lower the performance level of the
computing system 500 is.
According to embodiments of the present disclosure, whether a
failure occurs in any of the actual computing nodes 420, 430, . . .
, and 440 may be another indicator for performance level. A failure
may indicate a low performance level, while all the actual
computing nodes in a healthy state may indicate a high performance
level. In order to clearly reflect the performance of the computing
system 500, the performance level may be determined based on the
number of failed nodes and the total number of the normal computing
nodes.
At block 630, the capacity of the virtual node 510 is set based on
the obtained performance level, such that the scheduling node 450
allocates tasks to the virtual node 510 based on the capacity of
the virtual node. As the scheduling node 450 takes the virtual node
510 as an actual computing node and the scheduling node 450
allocates tasks to respective computing nodes 420, 430, . . . , and
440 based on respective capacity of the actual computing nodes, the
capacity of the virtual node 510 may be adjusted so as to control
task(s) allocated by the schedule node 450. At block 640, the
virtual node 510 may receive task(s) allocated by the scheduling
node 450.
Reference will be made to FIG. 7 for illustrating how to set the
capacity of the virtual node 510 based on the performance level of
the computing system 500. FIG. 7 depicts an example method 700 for
management of the virtual node 510 based on performance level of
the computing system 500, in accordance with embodiments of the
present disclosure. Operations at block 620 in FIG. 7 are the same
as that shown in FIG. 6, and blocks 720, 730 and 740 provide
detailed operations for setting the capacity of the virtual node
510. At block 710, it may be determined which criterion the
performance level meets.
According to embodiments of the present disclosure, if the obtained
performance level meeting a first criterion indicates a low
performance level of the computing system 500, the capacity of the
virtual node 510 may be set to be strong, such that the scheduling
node 450 may allocate tasks to the virtual node. Referring to FIG.
7, if the performance level meets the first criterion indicating a
low performance level of the computing system 500, then the method
700 may proceed to block 720.
At block 720, the capacity of the virtual node 510 may be set to be
strong, such as a better value to absorb task(s). In one
embodiment, the capacity of the virtual node 510 may be set to a
level better than capacity of at least one of the group of
computing nodes 420, 430, . . . , and 440, such that the scheduling
node 450 preferably allocates tasks to the virtual node 510 other
than to the actual computing nodes. Once the capacity of the
virtual node 510 is set to a level better than those of all the
actual computing nodes, the virtual node 510 may attract more tasks
allocated by the scheduling node 450.
With these embodiments of the present disclosure, the virtual node
510 with strong capacity may attract more tasks allocated by the
scheduling node 450 to it. Therefore, the actual computing nodes
420, 430, . . . , and 440 may continue work on processing the
previously allocated tasks without a need to worry about a drop in
their capacity. In this way, the normal operations of the actual
computing nodes 420, 430, . . . , and 440 may not be disturbed.
Continuing the above example for representing the performance of
the computing system 500 by the average response time, the first
criterion may be associated with a threshold time duration.
Supposing a response time longer than 0.05 second is unacceptable
to the user, then 0.05 second may be selected as the threshold. If
the average response time of the computing system 500 is above 0.05
second, then the virtual node 510 may be set to have strong
capacity, which indicates that the virtual node 510 is an idle
computing node that can be allocated tasks. At this point, the
scheduling node 450 may allocate more tasks to the virtual node
510, thereby reducing the number of tasks assigned to the actual
computing nodes and allowing the actual computing nodes to process
their tasks more efficiently.
Reference now to FIG. 8, depicted is an example diagram 800 for
setting capacity for the virtual node, in accordance with
embodiments of the present disclosure. The example diagram 800
shows a situation where the capacity is represented by a usage
ratio of the processing resource as described above. As the actual
computing nodes 420, 430, . . . , and 440 are busy in processing
tasks allocated to them, their usage ratio may be high, for
example, 60%, 60%, . . . , and 65%. At this point, the usage ratio
of the virtual node 510 may be set to 5% or another low value to
indicate strong capacity. During the operations of the computing
system 500, the usage ratio of both the virtual node 510 and the
actual computing nodes 420, 430, . . . , and 440 may be collected.
As the usage ratio of 5% is much lower than 60%, 60%, . . . , and
65%, the scheduling node 450 may allocate the newly received tasks
to the virtual node 510 instead of allocating them to any of the
actual computing nodes 420, 430, . . . , and 440.
According to embodiments of the present disclosure, once a task is
allocated to the virtual node 510 by the scheduling node 450, the
received task may be held in a queue associated with the virtual
node 510. Reference will be made to FIG. 9 for details, where FIG.
9 depicts an example diagram 900 for holding tasks in a task queue
910 associated with the virtual node 510, in accordance with
embodiments of the present disclosure. When the capacity of the
virtual node 510 is better than any of the other computing nodes
420, 430, . . . , and 440, the scheduling node 450 may continuously
allocate tasks in the task list 810 to the virtual node 510. Once
the virtual node 510 receives tasks, the virtual node 510 may hold
the received tasks in the task queue 910.
It is to be understood that the term "hold" means that the task is
just stored in the task queue 910 without really being processed by
the virtual node 510. Here, the task queue 910 is a storage space
for holding the excessive tasks that cannot be processed by the
actual computing nodes 420, 430, . . . , and 440 at the present
time. Once the performance of the computing system 500 returns to a
normal level, the tasks in the task queue 910 may be re-allocated
to the actual computing nodes 420, 430, . . . , and 440.
According to embodiments of the present disclosure, if the obtained
performance level meeting a second criterion for indicating both a
normal performance level of the computing system and at least one
task held in the virtual node, the scheduling node 450 may be
requested to re-allocate tasks allocated to the virtual node 510 to
other actual computing node(s). Referring back to FIG. 7, at block
710, if the obtained performance level meets a second criterion
that indicates an improvement in the performance of the computing
system 500 (e.g., a normal performance level), and at least one
task is held in the virtual node, the method 700 may proceed to
block 730. At block 730, the scheduling node 450 may be requested
to re-allocate tasks in the queue 910. Then tasks in the task queue
910 may be released such that the released tasks are re-allocated
by the scheduling node 450 among the group of actual computing
nodes 420, 430, . . . , and 440.
According to embodiments of the present disclosure, during the
re-allocation, tasks in the task queue 910 may be released
gradually, for example, one by one. In some embodiments, the tasks
in the task queue 910 may be released in batches. For example, 5%
(or another percentage) of the tasks may be released from the task
queue 910 in each batch. The performance of the computing system
500 may be periodically obtained by the virtual node. When the
obtained performance level still meets the second criterion, more
tasks may be released from the task queue 910. Once the obtained
performance level does not meet the second criterion (e.g., if the
performance level decreases), the releasing procedure may be
stopped until the performance level meets the second criterion
again. The gradual releasing may prevent the excessive tasks
released from the task queue 910 from impacting the performance of
the computing system 500. Therefore, the performance level may be
maintained at a reasonable level.
According to embodiments of the present disclosure, when the
performance is indicated by the average response time, the second
criterion may be associated with a second threshold lower than the
first threshold. In the above example, the second threshold may be
set to an average response time of 0.03 second or another value
indicating an improvement of the performance. Compared with the
first threshold of 0.05 second, the average response time of 0.03
becomes shorter, therefore it may indicate an improvement in the
performance of the computing system 500. Accordingly, if the
monitored average response time is below 0.03 second, then the
method 700 may proceed to the block 730 and start to release
task(s) in the task queue 910.
FIG. 10 depicts an example diagram 1000 for releasing a task in the
task queue 910 associated with the virtual node 510, in accordance
with embodiments of the present disclosure. Referring to FIG. 10,
the task list 810 of the scheduling node 450 comprises tasks 5, 6,
7, 8, . . . , N, and the task queue 910 comprises tasks 1, 2, 3,
and 4. Once the average response time of the computing system 500
is less than 0.03 second, one or more tasks may be released from
the task queue 910. Supposing the tasks are released one by one,
task 1 may be released first, and the released task 1 may be added
to the task list 810 and wait for re-allocation (as depicted by an
arrow 1010).
According to embodiments of the present disclosure, task 1 may be
inserted to the header of the task list 810 before task 5.
According to other embodiments of the present disclosure, the
released task 1 may be added at another location in the task list
810. For example, priorities may be set to each of these tasks in
the task list 810 and the task queue 910, and the released task 1
may be added into the task list 810 based on the priorities.
Afterwards, if the average response time is still below 0.03
second, then task 2 may be released from the task queue 910. In
some embodiments, the tasks may be prioritized according to, for
example, one or more of an importance of the task, a resource
utilization of the task (e.g., how many processing resources are
likely required to complete the task), or a time when the task was
sent to the virtual node (e.g., according to a first in, first out
process, a last in, first out process, etc.).
According to embodiments of the present disclosure, if the task
queue 910 is empty, the virtual node 910 may be deactivated in the
computing system 500. In this way, the processing and memory
resources for managing the virtual node 510 may be reduced in the
computing system 500. Therefore, more processing and memory
resources may be available in the computing system 500, which may
enhance the performance of the computing system 500. When the
performance of the computing system 500 goes down, the virtual node
510 may be activated for handling further tasks. Accordingly, these
embodiments may be used in situations where a performance level of
a stable computing system drops temporarily.
According to embodiments of the present disclosure, the task queue
910 may be maintained during operations of the computing system 500
even if the task queue 910 is empty. As activating and deactivating
the virtual node 510 may cause extra cost in the computing system
500, these embodiments may be used in situations where the
performance of the computing system 500 frequently changes.
According to embodiments of the present disclosure, if the obtained
performance level meets a third criterion indicating a high
performance level of the computing system and no task held in the
virtual node, the capacity of the virtual node 510 may be set to be
weak, for example, a level worse than capacity of one of the group
of computing nodes, such that tasks are prevented from being
allocated to the virtual node 510 by the scheduling node 450.
Referring back to FIG. 7, at block 710, if the obtained performance
information meets a third criterion indicating a normal performance
level of the computing system and no task held in the virtual node,
the method 700 may proceed to block 740. At block 740, the capacity
of the virtual node 510 may be set to be weak, for example, a worse
level to reject further tasks being allocated to the virtual node
510. Specifically, the capacity of the virtual node 510 may be set
to a level worse than capacity of any actual computing nodes in the
group, such that tasks are prevented from being allocated to the
virtual node 510 by the scheduling node 450.
Supposing a response time shorter than 0.01 second is a quick
response, then 0.01 second may be selected as part of the third
criterion. If the average response time of the computing system 500
is below 0.01, then the capacity of the virtual node 510 may be set
to be weak, for example, a value worse than those of all the
computing nodes to pretend that the virtual node 510 is weak or a
busy computing node. At this point, the scheduling node 450 may
allocate no task to the virtual node 510, and all the tasks are
allocated to the actual computing nodes 420, 430, . . . , and
440.
When the capacity is represented by a usage ratio of the processing
resource, the capacity of the virtual node 510 may be set to, for
example, 95% or another high value. During the operation of the
computing system 500, as the performance of the whole computing
system 500 is high, the capacity of the actual computing nodes 420,
430, . . . , and 440 may be better than that of the virtual node
510. At this point, instead of allocating the newly received tasks
to the virtual node 510, the scheduling node 450 may allocate the
newly received tasks to one of the actual computing nodes 420, 430,
. . . , and 440. With the above embodiments, the operations of the
computing system 500 may be switched into a normal mode.
The present invention may be a system, a method, and/or a computer
program product. The computer program product may include a
computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that
can retain and store instructions for use by an instruction
execution device. The computer readable storage medium may be, for
example, but is not limited to, an electronic storage device, a
magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
Computer readable program instructions described herein can be
downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
Computer readable program instructions for carrying out operations
of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
These computer readable program instructions may be provided to a
processor of a general purpose computer, special purpose computer,
or other programmable data processing apparatus to produce a
machine, such that the instructions, which execute via the
processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
The computer readable program instructions may also be loaded onto
a computer, other programmable data processing apparatus, or other
device to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other device to
produce a computer implemented process, such that the instructions
which execute on the computer, other programmable apparatus, or
other device implement the functions/acts specified in the
flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the Figures illustrate the
architecture, functionality, and operation of possible
implementations of systems, methods and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of code, which comprises one or more
executable instructions for implementing the specified logical
function(s). It should also be noted that, in some alternative
implementations, the functions noted in the block may occur out of
the order noted in the figures. For example, two blocks shown in
succession may, in fact, be executed substantially concurrently, or
the blocks may sometimes be executed in the reverse order,
depending upon the functionality involved. It will also be noted
that each block of the block diagrams and/or flowchart
illustration, and combinations of blocks in the block diagrams
and/or flowchart illustration, can be implemented by special
purpose hardware-based systems that perform the specified functions
or acts, or combinations of special purpose hardware and computer
instructions.
The descriptions of the various embodiments of the present
invention have been presented for purposes of illustration, but are
not intended to be exhaustive or limited to the embodiments
disclosed. Many modifications and variations will be apparent to
those of ordinary skill in the art without departing from the scope
and spirit of the described embodiments. The terminology used
herein was chosen to best explain the principles of the
embodiments, the practical application or technical improvement
over technologies found in the marketplace, or to enable others of
ordinary skill in the art to understand the embodiments disclosed
herein.
* * * * *
References